They are able to learn anti-correlated instances, i.e., defaulting to "positive" labels until seeing a negative counter-example, which should not be possible for a correct MIL model.

Moreover,tofurther accelerate overgreedy descent methods, wepresent a new accelerated random search (ARS) algorithm that incorporates prior information, together with aconvergence analysis.

Sharma et al. (2022) provide Y ang et al. (2022a) integrate Local SGDA with stochastic gradient estimators to eliminate the More recently, Zhang et al. (2023) adopt compressed momentum methods with Local SGD to increase the communication efficiency of the algorithm. For centralized nonconvex minimax problems, Y ang et al. (2022b) show that, even in deterministic settings, GDA-based methods necessitate the timescale separation of the stepsizes for primal and dual updates.

We consider a standard federated learning (FL) setup where a group of clients periodically coordinate with a central server to train a statistical model.